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Energy Optimization for Software-Defined Data Center

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Energy Optimization for Software-Defined Data Center electronics Article Energy Optimization for Software-Defined Data Center Networks Based on Flow Allocation Strategies Zebin Lu 1,2 , Junru Lei 2, Yihao He 2, Zhengfa Li 2, Shuhua Deng 2 and Xieping Gao 2,3,* 1 School of Mathematics and Computational Science, Xiangtan University, Xiangtan 411105, China; [email protected] 2 Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan 411105, China; [email protected] (J.L.); [email protected] (Y.H.); [email protected] (Z.L.); [email protected] (S.D.) 3 College of Software and Communication Engineering, Xiangnan University, Chenzhou 423043, China * Correspondence: [email protected] Received: 20 July 2019; Accepted: 7 September 2019; Published: 11 September 2019 Abstract: Nowadays, energy consumption has become an important issue in data center networks. The most promising energy-saving schemes are those that shut down unnecessary network devices and links while meeting the demand of traffic loads. Existing research mainly focuses on the strategies of energy savings in software-defined data center networks (SD-DCN). Few studies have considered both energy savings and the quality of service (QoS) of the traffic load. In this paper, we investigate the energy savings guaranteed by traffic load satisfaction ratio. To ensure the minimum-power consumption in data centers, we formulate the SD-DCN energy consumption optimization problem as an Integer Linear Programming model. To achieve a high success rate for traffic transmission, we propose three flow scheduling strategies. On this foundation, we propose a strategy-based Minimum Energy Consumption (MEC) heuristic algorithm to ensure the QoS satisfaction ratio in the process of energy optimization. The results show that our algorithm can save energy efficiently under the conditions of low traffic load and medium traffic load. Under high traffic load, our algorithm can achieve better network performance than existing solutions in terms of quality of service satisfaction ratio of flow allocation. Keywords: energy efficiency; software-defined data center networks; QoS; flow allocation 1. Introduction In the past decade, with the development of big data, cloud computing, virtualization, and other data-intensive applications, data centers are growing rapidly. While data centers significantly simplify the management of the application’s data, the traffic transmission in data centers brings great energy consumption. Studies have shown that the electricity consumption of global data centers accounted for 1.1% to 1.5% of the total electricity consumption in 2010, and it can be predicted that the percentage will increase to 8% by 2020 [1,2]. In addition, the energy consumption of data center networks (DCNs) accounts for 20% of the total power consumption when the servers are fully utilized, but it will rise to 50% when servers utilization decreases to 15% [3]. Therefore, energy-saving in data centers has become one of the most important topics. A data center is a facility composed of a number of technical elements such as network devices, servers, storage systems, and more. To improve the performance and reliability of the network, some novel topological structures have been proposed, such as Fat-Tree [4], VL2 [5], DCell [6], BCube [7], etc. Since a large-scale data center contains a large number of servers, links, and bandwidth-intensive applications, it is not easy to realize flexible scheduling of network resources in traditional distributed Electronics 2019, 8, 1014; doi:10.3390/electronics8091014 www.mdpi.com/journal/electronics Electronics 2019, 8, 1014 2 of 16 network architectures. Software-defined networking (SDN) is an emerging network architecture [8] that allows network operators to manage and control a network through a centralized controller. Since the control plane is separated from the underlying network device, it makes network management more efficient and convenient. Recently, some studies incorporated SDN into DCNs, named software-defined data center networks (SD-DCNs), to optimize the data center energy consumption by utilizing the scalability and manageability of SDN technology [9,10]. Some studies have proved that software-defined networking is an effective solution to solve energy problems in DCNs [11–14]. The main idea of these works is to design some energy optimization models to save energy under the condition of using as few network resources as possible. While those works can effectively save energy in certain scenarios, they ignore limited network resources that would fail to satisfy the quality of service (QoS) requirements. Typically, the QoS requirements can be roughly classified into bottleneck and additive [15,16]. For a bottleneck requirement, the cost of the path is determined by the value of that constraint at the bottleneck resource, such as bandwidth, CPU, and Ternary Content Addressable Memory (TCAM) [17–20]. For an additive requirement, the cost of the end-to-end path is given by the sum of the values of each link along that path, such as delay and jitter [16,21,22]. Some QoS parameters are multiplicative, such as loss rate [23]. Some researchers classify loss rate as an additive requirement because loss rate can be expressed as an additive requirement in an indirect way [15]. The QoS requirements are summarized in Table1. Table 1. Classification of quality of service (QoS) requirements. Type QoS Requirements bottleneck bandwidth, CPU, and TCAM [17–20] additive delay, jitter, and loss rate [15,16,21–23] In this paper, we mainly focus on bottleneck requirements, including bandwidth and TCAM. The bandwidth resource is scarce in a data center. Besides, the TCAM size in SDN switches is limited. Using traffic aggregation and other ways to allocate as few network resources as possible will inevitably lead to traffic transmission failure due to insufficient resources [24]. We investigate the problem of how to ensure the efficiency of network traffic transmission while realizing the energy optimization of the data center network. More specifically, we optimize SD-DCN energy consumption with the objective of minimizing the energy cost of switches and links under the constraint of network resources. To improve the QoS satisfaction ratio, we divide the network traffic into elephant flow and mice flow. Then, three different traffic scheduling strategies are used for these two kinds of traffic. Combined with the energy optimization model, a method which can meet both energy-saving and QoS requirements is found. In general, our contributions are summarized as follows: • We formulate SD-DCN energy consumption optimization problem as an Integer Linear Programming (ILP) model. Besides, we propose three different flow scheduling strategies to improve the QoS satisfaction ratio; • We propose a strategy-based Minimum Energy Consumption (MEC) heuristic algorithm to ensure the QoS satisfaction ratio in the process of energy optimization; • We evaluate and discuss the strategy-based heuristic algorithm in terms of effectiveness. The rest of the paper is organized as follows: Some related work is discussed in Section2. The network model and problem statement are introduced in Section3. The strategy-based Minimum Energy Consumption (MEC) heuristic algorithm is proposed in Section4. Simulation results and analysis are shown in Section5. Section6 concludes the paper. Electronics 2019, 8, 1014 3 of 16 2. Related Work Data center network energy consumption is growing increasingly. In order to reduce energy consumption in data center networks, researchers have done a lot of work to achieve high energy efficiency from different levels. From the perspective of energy, Heller et al. [25] proposed ElasticTree, which dynamically controls the number of switches and links used in the network to meet the changing data center traffic loads and turns off unused network elements in the network to save energy. Jiang et al. [26] proposed an energy-aware data center network, which uses as few network devices as possible and turns off unused network devices to reduce energy consumption. Wang et al. [27] proposed a correlation-aware power optimization (CARPO) algorithm that combines link rate adaptation and correlation-aware traffic consolidation to maximize energy savings. With the development of SDN technology, more and more scholars use SDN to solve data center network energy problems. Tu et al. [11] proposed two energy-saving models for data centers based on SDN. These models can get better energy efficiency in different scenarios. Yoon et al. [12] proposed a power minimization model in a Fat-Tree data center network, and adopted the Simulated Annealing algorithm to obtain the solution. Wei et al. [13] designed an energy-efficient routing algorithm based on the multinomial logit model, and formalized the energy-efficient routing algorithms. Zeng et al. [14] put forward a minimum-activated switches algorithm in SD-DCN to save energy, while the limitations of SDN itself were taken into account, i.e., TCAM-size limitation. Xu et al. [24] proposed a data center network energy-saving algorithm that leveraged SDN technology to minimize energy consumption in data center networks and increase the utilization of switches. Li et al. [28] designed an energy-aware flow scheduling approach called exclusive routing (EXR) which occupies the links in a flow independently based on priority. Existing research efforts mainly applied SDN technology to turn off network devices to save energy. However, several unique features
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